We present a case of a giant intra-abdominal pseudocyst in a 24-year-old male as a complication of ventriculoperitoneal (VP) shunt. Ultrasonography and computed tomography abdomen detected a 20 × cm 14.5× cm 9 cm thin-walled cystic lesion with few septae occupying a large space in the left side of the abdomen with a VP shunt tip within it. Histopathological findings suggested a pseudocyst. However, multiple epithelioid cell granulomas on cyst wall resulted in a diagffignostic dilemma.The psychological construct of hope is an important determinant for mental health and well-being. The availability of valid and reliable instruments to measure hope is, therefore, critical. Despite a large number of psychometric studies on the Herth Hope Index (HHI), its construct validity has not yet been determined. Therefore, this paper aimed to conduct a systematic review of the psychometric properties of the HHI.
Databases such as PubMed, Science Direct, Google Scholar, Magiran, SID, IranDoc, and IranMedex were evaluated systematically using the terms "HHI," "psychometric," "validity," "reliability," and related terms (with the use of OR and AND operators) and no restrictions on the year of publication. A total of 13 eligible studies were found published between 1992 and 2018 in the USA, Portugal, Switzerland, Iran, Germany, Petersburg, Japan, the Netherlands, Lima, Peru, and Norway. The methodology used in the available studies included principal component analysis (n = 6), maximum likelihood estimation (n = 5), and principal axis factoring (n = 1). One study did not point the methodology.
Four studies reported the total extracted variances to be less than 50%, six studies reported variance between 50% and 60%, and three papers reported variance that exceeded 60%. Of the papers that examined the factor structure of the HHI, two studies reported a one-factor solution, seven reported two factors, and four reported a three-factor solution. Although the HHI is the most widely translated and psychometrically tested tool in languages other than English, psychometric variations in factor solutions remain inconsistent.
Findings highlight the need for future research that appraises the validity of the HHI in different countries, and how the measure relates to other scales that evaluate hope.
Findings highlight the need for future research that appraises the validity of the HHI in different countries, and how the measure relates to other scales that evaluate hope.Publications are the cornerstone of the dissemination of scientific innovation and scholarly work, but published works are mostly behind paywalls. https://www.selleckchem.com/TGF-beta.html Therefore, many researchers and institutions are searching for alternative models for disseminating scholarly work that bypasses the current structure of paywalls. This study aimed to determine whether a self-published open access (OA) journal, the International Journal of Health Sciences (IJHS), has been able to reach a global audience in terms of authorship, readership, and impact using the OA model.
All IJHS articles were retrieved and analyzed using scientometric methods. Using the keywords from abstracts and titles, unsupervised clustering was performed to map research trends. Network analysis was used to chart the network of collaboration. The analysis of articles' metadata and the visualizations was performed using R programming language.
Using Google Scholar as a source, the general statistics of IJHS from inception to 2019 showed that the average citation per article was 11.29, and the impact factor of the journal was 2.28. The results demonstrate the obvious local and global impact of a locally published journal that allows unrestricted OA and uses an open source publishing platform. The journal's success at attracting diverse topics, authors, and readers is a testament to the power of the OA model.
Open source is feasible and rewarding and enables a global reach for research from under-represented regions. Local journals can help the Global South disseminate their scholarly work, which is frequently ignored by commercial and established publications.
Open source is feasible and rewarding and enables a global reach for research from under-represented regions. Local journals can help the Global South disseminate their scholarly work, which is frequently ignored by commercial and established publications.The present study is considered the first study that aims to estimate the spread of coronavirus disease (COVID)-19 pandemic in the Eastern Mediterranean Region and to predict the pattern of spread among Kingdom of Saudi Arabia (KSA) in comparison to Iran and Pakistan.
Data during the period from January 29, 2020, till April 14, 2020, were extracted from 76 WHO situational reports and from the Worldometer website. Numbers of populations in each country were considered during data analysis. Susceptible, infectious, recovered, and deaths (SIRD) model and smoothing spline regression model were used to predict the number of cases in each country.
SIRD model in KSA yielded β = 2e-0.6, γ = 0.006, and μ = 0.00038 and R= 0.00029. It is expected that by the 1of May 2020, that number of cumulative infected cases would rise to 16848 in KSA and to 11,825 in Pakistan while in Iran, it is expected that the number mostly will be 100485. Moreover, the basic reproduction number Ris expected to decrease by time progression.
The cumulative infected cases are expected to grow exponentially. Although Ris expected to be decreased, the quarantine measures should be maintained or even enhanced.
The cumulative infected cases are expected to grow exponentially. Although R0 is expected to be decreased, the quarantine measures should be maintained or even enhanced.In late 2019, a novel respiratory disease was identified as it began to spread rapidly within China's Hubei Province soon thereafter, being designated coronavirus disease 2019 (COVID-19). Unfortunately, trends in cases and rates of infection have been consistently misunderstood, particularly within the media, due to little, if any, statistical analysis of trends. Critical analysis of data is necessary to determine how to best manage local restrictions, particularly if there are resurgences of infection. As such, researchers have been calling for data-driven, statistical analysis of trends of disease to provide more context and validity for significant policy decisions.
This quantitative study sought to explore different statistical methods that can be used to evaluate trend data to improve decision-making and public information on the spread of COVID-19. Analyses were conducted using Spearman's rho, Mann-Whitney U tests, Mann-Kendal tests, and Augmented Dickey-Fuller tests with follow up Kwiatkowski-Phillips-Schmidt-Shin tests.